Ilya Sutskever (OpenAI Chief Scientist) on Open Source vs Closed AI Models
Summary
TLDR视频讨论了开源和非开源大型语言模型(LLM)之间的差异,指出尽管开源模型可能会逐渐接近GPT-4的能力,但由于技术和研究的持续进步,私有模型(如GPT-4)与开源模型之间的差距可能会继续扩大。讨论还涉及了GPT-4在经过处理以避免法律问题和提高用户友好性后,如何保留其原有能力的挑战。此外,提到了对更灵活、可定制化模型的需求,以及开发团队正致力于实现这一目标的努力。
Takeaways
- 🔍 开源与非开源模型之间的比较不应该是绝对的黑白分明,不存在某种永远无法被复现的秘密技术。
- 🌐 尽管开源模型有可能在未来达到GPT-4的能力,但那时商业公司可能已经开发出更为强大的模型。
- 📈 开源模型与私有模型之间的差距可能会随时间增加,因为开发这样的神经网络所需的努力、工程和研究量在不断增长。
- 🛠️ 开源模型越来越难以由小团队的研究人员和工程师产出,而更可能是大公司的专利。
- 🧠 在对模型进行调整(如去除某些功能)的过程中,会损失一些重要的能力,团队正在研究如何尽可能地保留这些能力。
- 🤖 基础模型(GPT-4的初始版本)使用起来并不那么简单,目标是开发出既能遵循指令又能给用户尽可能多控制权和能力的模型。
- 📏 对于用户需求有更大的灵活性和自定义能力的模型正在研究中,以避免法律问题同时满足用户需求。
- 💡 已经发现了一些方法来处理模型拒绝提供帮助的情况,以提升用户体验。
- 🚀 随着技术的进步,对于更加灵活和可自定义的模型的需求日益增长。
- ⏳ 即使是开源社区,随着时间的推移和技术的发展,也可能逐渐缩小与私有模型之间的差距。
Q & A
开源大型语言模型能否匹配GPT-4的能力,还是说GPT-4有不为人知的秘密来源?
-开源大型语言模型有可能在未来匹配GPT-4的能力,但并非因为GPT-4有不为人知的秘密来源。随着时间的推移,私有模型可能会更加强大,因此即便开源模型达到了GPT-4的水平,私有模型也会更进一步。
安装Stable Vicuna 13亿+版本是否是浪费时间?
-不一定是浪费时间。虽然目前的开源模型可能无法完全达到私有模型如GPT-4的水平,但它们依然具有价值,可以用于多种应用并提供重要的学习和研究机会。
开源模型与非开源模型之间的差距是否会越来越大?
-是的,由于私有模型背后的公司能够投入更多的资源进行研究和工程开发,这种差距可能会随时间增加。
为什么小团队越来越难以生产开源模型?
-因为生产这样的神经网络所需的努力、工程和研究量不断增加,这使得小团队越来越难以承担相关成本和资源需求。
GPT-4在被“切除”前的基础模型是什么样的?
-基础模型具有强大的能力,但在经过修改以增加用户指令遵循性和法律合规性后,可能会丧失某些能力。
为什么要修改GPT-4的基础模型?
-修改旨在使模型更易于使用、更好地遵循指令,并让用户有更多控制权,同时避免法律问题。
OpenAI是如何考虑模型的法律合规性的?
-OpenAI通过对模型进行调整,以减少法律风险,并确保模型的使用不会引起法律问题。
用户对于更灵活的模型有哪些需求?
-用户希望有更多的自定义选项,以便根据自己的特定需求调整模型的行为和输出。
OpenAI如何响应对更灵活模型的需求?
-OpenAI正在研究如何保留模型的关键能力,同时提供更多的自定义选项,以满足用户对灵活性和控制性的需求。
开源模型能否在未来追赶上私有模型的发展?
-虽然开源模型可能会逐渐接近私有模型的能力,但私有模型由于拥有更多的资源和研究投入,可能会持续保持领先。
Outlines
🤔开源LLM与GPT-4的对比
这段讨论了开源大型语言模型(LLM)与非开源模型(如GPT-4)之间的差异,特别是是否存在某种“秘密配方”让GPT-4独一无二。讲者表明,虽然目前开源模型可能无法与GPT-4相匹配,但这并非因为有不可获取的秘密成分,而是因为私有模型(如GPT-4)所拥有的资源和研发能力远超开源社区。他强调了私有模型和开源模型之间的差距可能会随时间而增大,因为开发这样的先进神经网络需要越来越多的努力、工程和研究。讲者还提到了围绕GPT-4基础模型的开发,特别是在通过降低模型能力(“lobotomize”)以避免法律问题的同时,如何尽可能保留其功能性。最后,讲者提出了对更灵活、可定制模型的需求,表达了团队正在探索如何实现这一目标的意向。
Mindmap
Keywords
💡开源LLM
💡GPT-4
💡技术进步
💡秘密配方
💡性能差距
💡基础模型
💡定制化
💡法律问题
💡指令遵循
💡研究与工程
Highlights
Open source LLMs vs. private models: understanding the differences and potential future.
No 'secret source' in GPT-4, but continuous advancements make it powerful.
Open source models could eventually match GPT-4, but there will always be a technology gap.
The gap between open source and private models may increase over time.
Significant engineering and research are required to produce advanced neural networks.
Advanced LLMs becoming less accessible for small groups due to increasing complexity.
Large companies are likely to remain at the forefront of advanced LLM development.
Base model of GPT-4 and its capabilities before and after optimization.
Challenges in maintaining capabilities while making LLMs safer and legally compliant.
Acknowledgment of the need for more flexible and customizable models.
Efforts to balance user control, model capabilities, and legal considerations.
Exploration of how to preserve model capabilities during refinement processes.
Desire for LLMs that follow instructions accurately and give users maximum control.
Legal and ethical challenges in LLM development and deployment.
Research and development focus on making LLMs more user-friendly and versatile.
Transcripts
both of you the question is could the
open source llm potentially match GPT
4's abilities without additional
technical
advances or is there a secret Source in
GPT 4 unknown to the world that sets its
apart from other models or am I wasting
my time installing stable
vicuna 13 billion plus wizard am I
wasting my time tell
me
[Applause]
all right
so to the open source versus non-op
Source models question you don't want to
think about it in in binary black and
white terms where like there is a secret
source that you'll never be
rediscovered what I will say or whether
GPT 4 will ever be produced by open
source models perhaps one day it will be
but when it will be there will be a much
more powerful model in the companies so
there will always be a gap between the
open source models and the private
models and this Gap may even be
increasing this time the amount of
effort and engineering and research that
it takes to produce one such neural net
keeps
increasing and so even if there are open
source models they will never be they
will be less and less produced by small
groups of of dedicated researchers and
engineers and it will only be the
Providence of a company a big
company Hi H can you tell us more about
the base model before you lobotomized it
lined
it what with the base model of GPT 4
what about it how was it before you
lobotomized it uh
we we we definitely realize that in the
process of doing rhf on the models it
loses important capability we're
studying how we can preserve as much of
that as possible um the base model is
like not that easy to use um but what
we'd like to get to is something that
does follow instructions and gives users
as much control and as much capability
as possible doesn't get
and doesn't get us in legal trouble
although like you know we've discovered
a lot of stuff like refusals to help
with that so we want we we totally hear
the request for more flexible models um
and we're trying to figure out how to do
that and and give users more
customization over them
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